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electricsheepasia/asia-climate-hdx-hapi-afghanistan

HDX HAPI Data for Afghanistan Publisher: HDX Humanitarian API Data · Source: HDX · License: hdx-other · Updated: 2026-02-18 Abstract This dataset contains data obtained from the HDX Humanitarian API (HDX HAPI), which provides standardized humanitarian indicators designed for seamless interoperability from multiple sources. The data facilitates automated workflows and visualizations to support humanitarian decision making. For more information, please see the HDX… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepasia/asia-climate-hdx-hapi-afghanistan.

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Dataset Card

HDX HAPI Data for Afghanistan

Publisher: HDX Humanitarian API Data · Source: HDX · License: hdx-other · Updated: 2026-02-18


Abstract

This dataset contains data obtained from the HDX Humanitarian API (HDX HAPI), which provides standardized humanitarian indicators designed for seamless interoperability from multiple sources. The data facilitates automated workflows and visualizations to support humanitarian decision making. For more information, please see the HDX HAPI landing page and documentation.

Each row in this dataset represents subnational administrative unit observations. Temporal coverage is indicated by the reference_period_start, reference_period_end column(s). Geographic scope: AFG.

Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).


Dataset Characteristics

DomainFood security and nutrition
Unit of observationSubnational administrative unit observations
Rows (total)6,170
Columns18 (3 numeric, 11 categorical, 2 datetime)
Train split4,936 rows
Test split1,234 rows
Geographic scopeAFG
PublisherHDX Humanitarian API Data
HDX last updated2026-02-18

Variables

Geographic — location_code (AFG), provider_admin1_name (Nangarhar, Badakhshan, Kunar), provider_admin2_name (Baharak, Jani Khel, Arghandab), admin1_code (AF06, AF17, AF15), admin1_name (Nangarhar, Badakhshan, Kunar) and 5 others.

Temporal — reference_period_start, reference_period_end.

Identifier / Metadata — esa_source (HDX), esa_processed.

Other — has_hrp, in_gho, operation (Returns and displacement in Afghanistan, Climate and mobility in Afghanistan), reporting_round (range 1.0–16.0).


Quick Start

python
from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/asia-climate-hdx-hapi-afghanistan")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

Schema

ColumnTypeNull %Range / Sample Values
location_codeobject0.0%AFG
has_hrpbool0.0%
in_ghobool0.0%
provider_admin1_nameobject0.3%Nangarhar, Badakhshan, Kunar
provider_admin2_nameobject8.3%Baharak, Jani Khel, Arghandab
admin1_codeobject0.3%AF06, AF17, AF15
admin1_nameobject0.3%Nangarhar, Badakhshan, Kunar
admin2_codeobject8.3%AF0601, AF0615, AF0616
admin2_nameobject8.3%Dawlat Abad, Baharak, Jani Khel
admin_levelint640.0%0.0 – 2.0 (mean 1.9143)
operationobject0.0%Returns and displacement in Afghanistan, Climate and mobility in Afghanistan
assessment_typeobject0.0%BA
populationint640.0%6.0 – 6557855.0 (mean 31844.3091)
reporting_roundint640.0%1.0 – 16.0 (mean 8.8417)
reference_period_startdatetime64[ns]0.0%
reference_period_enddatetime64[ns]0.0%
esa_sourceobject0.0%HDX
esa_processedobject0.0%

Numeric Summary

ColumnMinMaxMeanMedian
admin_level0.02.01.91432.0
population6.06557855.031844.30913809.5
reporting_round1.016.08.84179.0

Curation

Raw data was downloaded from HDX via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snake_case. Common missing-value markers (N/A, null, none, -, unknown, no data, #N/A) were unified to NaN. 2 column(s) were cast from string to numeric or datetime based on parse-success rate (>85% threshold). The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.


Limitations

  • —Data originates from HDX Humanitarian API Data and has not been independently validated by ESA.
  • —Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
  • —Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

Citation

bibtex
@dataset{hdx_asia_climate_hdx_hapi_afghanistan,
  title     = {HDX HAPI Data for Afghanistan},
  author    = {HDX Humanitarian API Data},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/hdx-hapi-afg},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.